• Intro/Context
  • Forests are globally important
  • Anthropocence effects
  • Global forest loss and gain and change
    • Global greening = India(Agriculture) + China(Forests)
• Economics*Ecology = Landscape Extended Models
• Network Analysis of China’s Greening
  • Global Scale
  • Local Scale
    • Landscape = Chen 2019
    • Resilience Analysis of China’s Forest LE-MRIO
• Conclusions and Future Work
• Acknowledgements

• biodiversity
• water and nutrient cycling
• carbon storage
• resources(wood, food)
• culturally

• Humans = dominant global impact -> Anthropocene
• Global = Climate Change
• Indirect Effects Significant

• Economic trade data is a window into human impacts
• Brief history of IO and ENA analyses
• Global Trade Models
• Trade Networks MRIO = Sectors + Regions
• Environmental Extensions
• Forested Landscapes and Embodied Trade Networks

• Indirect effecs and The far reach of the city
• Complex systems = many players and indirect effects matter (surprising)

• Ecological network theory provides predictions and metrics (Lau 2017)
• Systems theory provides strategies for inteventions
• ENA <- Odums, MacArthur, Ulanowicz, Patten,
• SNA -> ecological networks (Watts and Strogatz, etc.)
• Structure linked to function (Donella Meadows)

• Work = Forest Land Embodied in Trade

• India is greening agriculturally

• China is greening through reforestation

• China is big and diverse (Tropical to Alpine/Boreal)
• Long history of human habitation in China
• Historically, two primary regions of forestry
• Forest conservation impacts harvest
• Flows within China and among countries globally important

• Global Scale
• Local Scale
  • Landscape = Chen 2019
  • Resilience Analysis of China’s Forest LE-MRIO

• Tian et al. 2019 showed that China consumes an equivalent amount of domestic cropland as forest land, on the order of 10\(^6\) km\(^2\).
• Looking at the domestic landuse productivity data for China, forests have the lowest monetary productivity.
• Thus, per unit monetary output a relatively larger amount of forest land is used.

• Tian et al. 2019 showed that China consumes an equivalent amount of domestic cropland as forest land, on the order of 10\(^6\) km\(^2\).
• Looking at the domestic landuse productivity data for China, forests have the lowest monetary productivity.
• Thus, per unit monetary output a relatively larger amount of forest land is used.

• How do we quantify and manage systems?
• Input-Output Analysis provides a modeling framework
• Direct consumption
• Trade occurs among sectors == Indirect consumption
• IO and MRIO models
• A new equation for a new era in science E = F(I-A)^-1

• Allows for indirect/consumption based accounting

• Information

• Related to Shannon Information/Diversity index
• \[H = -\sum_i^n p_i log(p_i)\]

NEED TO ADD FIGURE WITH DATA FLOWS

Maybe check the Mi 2018 supp mat

1. What research has been done on forest or forest landscape embodied networks?
2. What is the network structure? How can we characterize it?
3. What can we say about the potential system dynamics based on network structure?

• LEMRIO global (Tian 2019)
• LEMRIO local (Chen 2019)
• Your LE-MRIO China
• Your ENA analysis
  • Small world
  • Modularity
  • Centrality
  • Control
  • Resilience

• Analysis = Structure = Robustness

• Overly efficient = Brittle
• Overly redundant = Stagnant
• Both can lead to niche opennings
• Niches can then be filled by natural selection, adaptation or invasion

• Limitations of MRIO
• Potential impacts of storage lags and buffers

• Next up = Climate change impacts and global scale